class_docstr: Cost model to predict the speed of a config
docstr: Fit training data from log.
  
  Parameters
  ----------
  records: Array of Tuple(MeasureInput, MeasureResult)
      The tuning records
  plan_size: int
      The plan size of tuner
  min_seed_records: int
      Defaults to 500. Indicates the minimum number of records to
      train the tuner with. If there are less than `min_seed_records`
      number of records in `data_set`, no training of the tuner
      will be done.
docstr_tok: ["Fit","training","data","from","log",".","Parameters","--","--","--","--","--","records",":","Array","of","Tuple","(","MeasureInput",",","MeasureResult",")","The","tuning","records","plan_size",":","int","The","plan","size","of","tuner","min_seed_records",":","int","Defaults","to","500",".","Indicates","the","minimum","number","of","records","to","train","the","tuner","with",".","If","there","are","less","than","`","min_seed_records","`","number","of","records","in","`","data_set","`",",","no","training","of","the","tuner","will","be","done","."]
code: def fit_log(self, records, plan_size, min_seed_records=500):
      """Fit training data from log.
  
      Parameters
      ----------
      records: Array of Tuple(MeasureInput, MeasureResult)
          The tuning records
      plan_size: int
          The plan size of tuner
      min_seed_records: int
          Defaults to 500. Indicates the minimum number of records to
          train the tuner with. If there are less than `min_seed_records`
          number of records in `data_set`, no training of the tuner
          will be done.
      """
      raise NotImplementedError()
  
imports: import gc
  import numpy
  from .tuner import Tuner
  from ..env import GLOBAL_SCOPE
label_desc: Use image node_id 3 for calling the CostModel obj's underlying member method code with example usage: obj.fit_log(records, plan_size, min_seed_records) without return types